Optimization of Water Level from Great Lakes Based on Vector Autoregressive Model and Goal Programming Model
Bibliographic record
Abstract
The Great Lakes, the largest group of freshwater lakes in the world, have profound impacts on residents, ecosystems, water resource utilization, shipping, and tourism industries. Addressing water level variability, this study integrates network science, goal programming algorithm, and Model Predictive Control to establish a comprehensive and adaptive model for optimizing dam adjustment mechanisms and maximizing stakeholder benefits. Initially, a Vector Autoregression Model is developed for the Great Lakes and connecting river flows toward the Atlantic Ocean to conditionally project future paths of specified variables. This model yields a network representation of the Great Lakes system. Subsequently, a Goal Programming Model is constructed to determine optimal water levels throughout the year based on extensive literature review and priority rankings. Leveraging insights from the 2014 plan, a detailed analysis is conducted on Lake Ontario water levels, focusing solely on stakeholders and influential factors. This research contributes a robust methodology for managing water levels in the Great Lakes region, providing valuable insights for sustainable water resource management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".